{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000019.SZ   20241225   6.63   6.64   6.45   6.57       6.62   -0.05   \n",
      "1     000019.SZ   20241224   6.58   6.64   6.54   6.62       6.57    0.05   \n",
      "2     000019.SZ   20241223   6.77   6.79   6.55   6.57       6.75   -0.18   \n",
      "3     000019.SZ   20241220   6.80   6.88   6.72   6.75       6.80   -0.05   \n",
      "4     000019.SZ   20241219   6.81   6.83   6.66   6.80       6.85   -0.05   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3398  000019.SZ   20100108  10.15  10.34  10.01  10.27      10.18    0.09   \n",
      "3399  000019.SZ   20100107  10.71  10.80  10.05  10.18      10.70   -0.52   \n",
      "3400  000019.SZ   20100106  10.61  11.05  10.56  10.70      10.60    0.10   \n",
      "3401  000019.SZ   20100105  10.52  10.65  10.37  10.60      10.50    0.10   \n",
      "3402  000019.SZ   20100104  10.44  10.70  10.36  10.50      10.43    0.07   \n",
      "\n",
      "      pct_chg       vol      amount  \n",
      "0     -0.7553  63164.42  41187.7670  \n",
      "1      0.7610  72105.70  47529.2920  \n",
      "2     -2.6667  89027.36  59046.1870  \n",
      "3     -0.7353  63141.76  42787.0160  \n",
      "4     -0.7299  89993.60  60695.3020  \n",
      "...       ...       ...         ...  \n",
      "3398   0.8800  18759.81  19098.5441  \n",
      "3399  -4.8600  34092.68  35359.0952  \n",
      "3400   0.9400  47669.11  51657.4861  \n",
      "3401   0.9500  31108.44  32745.9017  \n",
      "3402   0.6700  30239.10  31902.9915  \n",
      "\n",
      "[3403 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('48bcb46cfeb72e22e51939e74a6fbb7493bd578867a7ffff778e518f')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000019.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000019.SZ   20241225   6.63   6.64   6.45   6.57       6.62   -0.05   \n",
      "1     000019.SZ   20241224   6.58   6.64   6.54   6.62       6.57    0.05   \n",
      "2     000019.SZ   20241223   6.77   6.79   6.55   6.57       6.75   -0.18   \n",
      "3     000019.SZ   20241220   6.80   6.88   6.72   6.75       6.80   -0.05   \n",
      "4     000019.SZ   20241219   6.81   6.83   6.66   6.80       6.85   -0.05   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3398  000019.SZ   20100108  10.15  10.34  10.01  10.27      10.18    0.09   \n",
      "3399  000019.SZ   20100107  10.71  10.80  10.05  10.18      10.70   -0.52   \n",
      "3400  000019.SZ   20100106  10.61  11.05  10.56  10.70      10.60    0.10   \n",
      "3401  000019.SZ   20100105  10.52  10.65  10.37  10.60      10.50    0.10   \n",
      "3402  000019.SZ   20100104  10.44  10.70  10.36  10.50      10.43    0.07   \n",
      "\n",
      "      pct_chg       vol      amount  \n",
      "0     -0.7553  63164.42  41187.7670  \n",
      "1      0.7610  72105.70  47529.2920  \n",
      "2     -2.6667  89027.36  59046.1870  \n",
      "3     -0.7353  63141.76  42787.0160  \n",
      "4     -0.7299  89993.60  60695.3020  \n",
      "...       ...       ...         ...  \n",
      "3398   0.8800  18759.81  19098.5441  \n",
      "3399  -4.8600  34092.68  35359.0952  \n",
      "3400   0.9400  47669.11  51657.4861  \n",
      "3401   0.9500  31108.44  32745.9017  \n",
      "3402   0.6700  30239.10  31902.9915  \n",
      "\n",
      "[3403 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock000019.csv\")\n",
    "stockname='深深宝A'\n",
    "stockcode='000019'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3393</th>\n",
       "      <th>3394</th>\n",
       "      <th>3395</th>\n",
       "      <th>3396</th>\n",
       "      <th>3397</th>\n",
       "      <th>3398</th>\n",
       "      <th>3399</th>\n",
       "      <th>3400</th>\n",
       "      <th>3401</th>\n",
       "      <th>3402</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "      <td>000019.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>6.63</td>\n",
       "      <td>6.58</td>\n",
       "      <td>6.77</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.91</td>\n",
       "      <td>7.05</td>\n",
       "      <td>7.16</td>\n",
       "      <td>7.31</td>\n",
       "      <td>7.17</td>\n",
       "      <td>...</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.15</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.61</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>6.64</td>\n",
       "      <td>6.64</td>\n",
       "      <td>6.79</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.83</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.24</td>\n",
       "      <td>7.31</td>\n",
       "      <td>7.33</td>\n",
       "      <td>...</td>\n",
       "      <td>11.25</td>\n",
       "      <td>10.4</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.58</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.8</td>\n",
       "      <td>11.05</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>6.45</td>\n",
       "      <td>6.54</td>\n",
       "      <td>6.55</td>\n",
       "      <td>6.72</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.82</td>\n",
       "      <td>6.83</td>\n",
       "      <td>7.04</td>\n",
       "      <td>7.14</td>\n",
       "      <td>7.11</td>\n",
       "      <td>...</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.16</td>\n",
       "      <td>10.12</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.06</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.37</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>6.57</td>\n",
       "      <td>6.62</td>\n",
       "      <td>6.57</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.85</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.15</td>\n",
       "      <td>7.31</td>\n",
       "      <td>...</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.57</td>\n",
       "      <td>10.16</td>\n",
       "      <td>10.27</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>6.62</td>\n",
       "      <td>6.57</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.85</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.15</td>\n",
       "      <td>7.31</td>\n",
       "      <td>7.16</td>\n",
       "      <td>...</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.57</td>\n",
       "      <td>10.16</td>\n",
       "      <td>10.27</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-0.7553</td>\n",
       "      <td>0.761</td>\n",
       "      <td>-2.6667</td>\n",
       "      <td>-0.7353</td>\n",
       "      <td>-0.7299</td>\n",
       "      <td>-0.436</td>\n",
       "      <td>-2.9619</td>\n",
       "      <td>-0.8392</td>\n",
       "      <td>-2.1888</td>\n",
       "      <td>2.095</td>\n",
       "      <td>...</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>4.04</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>63164.42</td>\n",
       "      <td>72105.7</td>\n",
       "      <td>89027.36</td>\n",
       "      <td>63141.76</td>\n",
       "      <td>89993.6</td>\n",
       "      <td>81373.5</td>\n",
       "      <td>107684.7</td>\n",
       "      <td>76913.79</td>\n",
       "      <td>99333.89</td>\n",
       "      <td>141837.18</td>\n",
       "      <td>...</td>\n",
       "      <td>82279.94</td>\n",
       "      <td>18808.47</td>\n",
       "      <td>27238.23</td>\n",
       "      <td>23868.59</td>\n",
       "      <td>20752.45</td>\n",
       "      <td>18759.81</td>\n",
       "      <td>34092.68</td>\n",
       "      <td>47669.11</td>\n",
       "      <td>31108.44</td>\n",
       "      <td>30239.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>41187.767</td>\n",
       "      <td>47529.292</td>\n",
       "      <td>59046.187</td>\n",
       "      <td>42787.016</td>\n",
       "      <td>60695.302</td>\n",
       "      <td>55931.275</td>\n",
       "      <td>74521.907</td>\n",
       "      <td>54768.428</td>\n",
       "      <td>71583.224</td>\n",
       "      <td>102622.219</td>\n",
       "      <td>...</td>\n",
       "      <td>89739.387</td>\n",
       "      <td>19402.8979</td>\n",
       "      <td>28028.1148</td>\n",
       "      <td>24719.1339</td>\n",
       "      <td>21052.4528</td>\n",
       "      <td>19098.5441</td>\n",
       "      <td>35359.0952</td>\n",
       "      <td>51657.4861</td>\n",
       "      <td>32745.9017</td>\n",
       "      <td>31902.9915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3403 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1          2          3          4          5     \\\n",
       "ts_code     000019.SZ  000019.SZ  000019.SZ  000019.SZ  000019.SZ  000019.SZ   \n",
       "trade_date   20241225   20241224   20241223   20241220   20241219   20241218   \n",
       "open             6.63       6.58       6.77        6.8       6.81       6.91   \n",
       "high             6.64       6.64       6.79       6.88       6.83       6.95   \n",
       "low              6.45       6.54       6.55       6.72       6.66       6.82   \n",
       "close            6.57       6.62       6.57       6.75        6.8       6.85   \n",
       "pre_close        6.62       6.57       6.75        6.8       6.85       6.88   \n",
       "change          -0.05       0.05      -0.18      -0.05      -0.05      -0.03   \n",
       "pct_chg       -0.7553      0.761    -2.6667    -0.7353    -0.7299     -0.436   \n",
       "vol          63164.42    72105.7   89027.36   63141.76    89993.6    81373.5   \n",
       "amount      41187.767  47529.292  59046.187  42787.016  60695.302  55931.275   \n",
       "\n",
       "                 6          7          8           9     ...       3393  \\\n",
       "ts_code     000019.SZ  000019.SZ  000019.SZ   000019.SZ  ...  000019.SZ   \n",
       "trade_date   20241217   20241216   20241213    20241212  ...   20100115   \n",
       "open             7.05       7.16       7.31        7.17  ...      10.71   \n",
       "high             7.11       7.24       7.31        7.33  ...      11.25   \n",
       "low              6.83       7.04       7.14        7.11  ...      10.69   \n",
       "close            6.88       7.09       7.15        7.31  ...      10.76   \n",
       "pre_close        7.09       7.15       7.31        7.16  ...      10.38   \n",
       "change          -0.21      -0.06      -0.16        0.15  ...       0.38   \n",
       "pct_chg       -2.9619    -0.8392    -2.1888       2.095  ...       3.66   \n",
       "vol          107684.7   76913.79   99333.89   141837.18  ...   82279.94   \n",
       "amount      74521.907  54768.428  71583.224  102622.219  ...  89739.387   \n",
       "\n",
       "                  3394        3395        3396        3397        3398  \\\n",
       "ts_code      000019.SZ   000019.SZ   000019.SZ   000019.SZ   000019.SZ   \n",
       "trade_date    20100114    20100113    20100112    20100111    20100108   \n",
       "open             10.19        10.3        10.1       10.28       10.15   \n",
       "high              10.4       10.55       10.58       10.36       10.34   \n",
       "low              10.16       10.12        10.1       10.06       10.01   \n",
       "close            10.38       10.19       10.57       10.16       10.27   \n",
       "pre_close        10.19       10.57       10.16       10.27       10.18   \n",
       "change            0.19       -0.38        0.41       -0.11        0.09   \n",
       "pct_chg           1.86        -3.6        4.04       -1.07        0.88   \n",
       "vol           18808.47    27238.23    23868.59    20752.45    18759.81   \n",
       "amount      19402.8979  28028.1148  24719.1339  21052.4528  19098.5441   \n",
       "\n",
       "                  3399        3400        3401        3402  \n",
       "ts_code      000019.SZ   000019.SZ   000019.SZ   000019.SZ  \n",
       "trade_date    20100107    20100106    20100105    20100104  \n",
       "open             10.71       10.61       10.52       10.44  \n",
       "high              10.8       11.05       10.65        10.7  \n",
       "low              10.05       10.56       10.37       10.36  \n",
       "close            10.18        10.7        10.6        10.5  \n",
       "pre_close         10.7        10.6        10.5       10.43  \n",
       "change           -0.52         0.1         0.1        0.07  \n",
       "pct_chg          -4.86        0.94        0.95        0.67  \n",
       "vol           34092.68    47669.11    31108.44     30239.1  \n",
       "amount      35359.0952  51657.4861  32745.9017  31902.9915  \n",
       "\n",
       "[11 rows x 3403 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2054f4e5490>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3597 entries, 0 to 3596\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3597 non-null   datetime64[ns]\n",
      " 1   open        3597 non-null   float64       \n",
      " 2   high        3597 non-null   float64       \n",
      " 3   low         3597 non-null   float64       \n",
      " 4   close       3597 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.6 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3594</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3595</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3592 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368\n",
       "3593 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389\n",
       "3594 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426\n",
       "3595 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467\n",
       "3596 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date  open  high   low  close\n",
      "0    2010-01-04  6.21  6.27  6.12   6.12\n",
      "1    2010-01-05  6.15  6.26  6.08   6.21\n",
      "2    2010-01-06  6.20  6.23  6.10   6.11\n",
      "3    2010-01-07  6.11  6.15  6.01   6.02\n",
      "4    2010-01-08  6.01  6.08  5.99   6.04\n",
      "...         ...   ...   ...   ...    ...\n",
      "3622 2024-12-19  8.52  8.58  8.40   8.48\n",
      "3623 2024-12-20  8.47  8.60  8.46   8.48\n",
      "3624 2024-12-23  8.45  8.73  8.45   8.62\n",
      "3625 2024-12-24  8.60  8.78  8.59   8.78\n",
      "3626 2024-12-25  8.78  9.02  8.77   8.88\n",
      "\n",
      "[3627 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3627 entries, 0 to 3626\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3627 non-null   datetime64[ns]\n",
      " 1   open        3627 non-null   float64       \n",
      " 2   high        3627 non-null   float64       \n",
      " 3   low         3627 non-null   float64       \n",
      " 4   close       3627 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.8 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "      <td>8.106333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "      <td>8.122333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "      <td>8.146333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "      <td>8.177333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "      <td>8.211000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10        30\n",
       "0    2010-01-04  6.21  6.27  6.12   6.12    NaN    NaN       NaN\n",
       "1    2010-01-05  6.15  6.26  6.08   6.21    NaN    NaN       NaN\n",
       "2    2010-01-06  6.20  6.23  6.10   6.11    NaN    NaN       NaN\n",
       "3    2010-01-07  6.11  6.15  6.01   6.02    NaN    NaN       NaN\n",
       "4    2010-01-08  6.01  6.08  5.99   6.04  6.100    NaN       NaN\n",
       "...         ...   ...   ...   ...    ...    ...    ...       ...\n",
       "3622 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368  8.106333\n",
       "3623 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389  8.122333\n",
       "3624 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426  8.146333\n",
       "3625 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467  8.177333\n",
       "3626 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534  8.211000\n",
       "\n",
       "[3627 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           8.78   9.02\n",
       "1           8.60   8.78\n",
       "2           8.45   8.73\n",
       "3           8.47   8.60\n",
       "4           8.52   8.58\n",
       "...          ...    ...\n",
       "3622        6.01   6.08\n",
       "3623        6.11   6.15\n",
       "3624        6.20   6.23\n",
       "3625        6.15   6.26\n",
       "3626        6.21   6.27\n",
       "\n",
       "[3627 rows x 2 columns]>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.12],\n",
       "       [6.21],\n",
       "       [6.11],\n",
       "       ...,\n",
       "       [8.62],\n",
       "       [8.78],\n",
       "       [8.88]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         open  high   low  close\n",
       "date                            \n",
       "14613.0  6.21  6.27  6.12   6.12\n",
       "14614.0  6.15  6.26  6.08   6.21\n",
       "14615.0  6.20  6.23  6.10   6.11\n",
       "14616.0  6.11  6.15  6.01   6.02\n",
       "14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...    ...\n",
       "20076.0  8.52  8.58  8.40   8.48\n",
       "20077.0  8.47  8.60  8.46   8.48\n",
       "20080.0  8.45  8.73  8.45   8.62\n",
       "20081.0  8.60  8.78  8.59   8.78\n",
       "20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  open  high   low  close\n",
       "0     14613.0  6.21  6.27  6.12   6.12\n",
       "1     14614.0  6.15  6.26  6.08   6.21\n",
       "2     14615.0  6.20  6.23  6.10   6.11\n",
       "3     14616.0  6.11  6.15  6.01   6.02\n",
       "4     14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...   ...    ...\n",
       "3622  20076.0  8.52  8.58  8.40   8.48\n",
       "3623  20077.0  8.47  8.60  8.46   8.48\n",
       "3624  20080.0  8.45  8.73  8.45   8.62\n",
       "3625  20081.0  8.60  8.78  8.59   8.78\n",
       "3626  20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2050ce540b0>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\python\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.0728 - val_loss: 0.0403\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0091 - val_loss: 0.0018\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 - val_loss: 8.8222e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.6725e-04 - val_loss: 0.0011\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.4494e-04 - val_loss: 0.0011\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3590e-04 - val_loss: 0.0010\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2712e-04 - val_loss: 0.0010\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2227e-04 - val_loss: 9.8603e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1661e-04 - val_loss: 9.7032e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1220e-04 - val_loss: 9.6015e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.0910e-04 - val_loss: 9.5310e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.0559e-04 - val_loss: 9.5560e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.0339e-04 - val_loss: 9.5942e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.0149e-04 - val_loss: 9.6601e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9894e-04 - val_loss: 9.7283e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9644e-04 - val_loss: 9.8124e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9374e-04 - val_loss: 9.8107e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8998e-04 - val_loss: 9.8124e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8579e-04 - val_loss: 9.7881e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8139e-04 - val_loss: 9.7377e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.7581e-04 - val_loss: 9.6446e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.6977e-04 - val_loss: 9.5522e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.6325e-04 - val_loss: 9.3981e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.5620e-04 - val_loss: 9.2177e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.4890e-04 - val_loss: 9.0770e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.4164e-04 - val_loss: 8.8960e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.3393e-04 - val_loss: 8.7634e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.2655e-04 - val_loss: 8.5532e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.1769e-04 - val_loss: 8.3533e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.0841e-04 - val_loss: 8.1291e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.02956\n",
      "均方根误差: 0.17192\n",
      "平均绝对误差: 0.13506\n",
      "R2: 0.91594\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
